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Combined mining is a hybrid mining approach for mining informative patterns from single or multipledata-sources, multiple-features extraction and applying multiple-methods as per the requirements. Datamining applications often involve complex data likemultiple heterogeneous data sources, different userpreference and create decision-making actions. Thecomplete useful information may not be obtained byusing single data mining method in the form of informative patterns as that would consume more time andspace. This paper implements hybrid or combined mining approach that applies Lossy-counting algorithmon each data-source to get the frequent data item-sets and then generates the combined association rules.Applying multi-feature approach, we generate incremental pair patterns and incremental cluster patterns.In multi-method combined mining approach, FP-growthand Bayesian Belief Network are combined togenerate classifier to get more informative knowledge. This paper uses two different data-sets to get moreuseful knowledge and compare the results

Pattern Generation for Complex Data Using Hybrid Mining